SOTAVerified

Inverse Rendering

Inverse Rendering is the task of recovering the properties of a scene, such as shape, material, and lighting, from an image or a video. The goal of inverse rendering is to determine the properties of a scene given an observation of it, and to generate new images or videos based on these properties.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 271 papers

TitleStatusHype
Plateau-reduced Differentiable Path Tracing—0
Learning to Rasterize DifferentiablyCode0
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing—0
Random Weight Factorization Improves the Training of Continuous Neural Representations—0
A General Scattering Phase Function for Inverse Rendering—0
PRIF: Primary Ray-based Implicit Function—0
Deep Uncalibrated Photometric Stereo via Inter-Intra Image Feature Fusion—0
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo—0
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated ImagesCode0
GAN2X: Non-Lambertian Inverse Rendering of Image GANs—0
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor ScenesCode0
TileGen: Tileable, Controllable Material Generation and Capture—0
Differentiable Rendering of Neural SDFs through Reparameterization—0
Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior—0
Physically-Based Editing of Indoor Scene Lighting from a Single Image—0
Physically Disentangled RepresentationsCode0
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images—0
Dressi: A Hardware-Agnostic Differentiable Renderer with Reactive Shader Packing and Soft Rasterization—0
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions—0
PANDORA: Polarization-Aided Neural Decomposition Of Radiance—0
Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks—0
CLA-NeRF: Category-Level Articulated Neural Radiance Field—0
Differentiable Neural Radiosity—0
Survey of Deep Learning Methods for Inverse Problems—0
Accelerating Inverse Rendering By Using a GPU and Reuse of Light Paths—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Neural-PBIRHDR-PSNR26.01—Unverified
2NVDiffRecMCHDR-PSNR24.43—Unverified
3InvRenderHDR-PSNR23.76—Unverified
4NeRFactorHDR-PSNR23.54—Unverified
5NeRDHDR-PSNR23.29—Unverified
6NVDiffRecHDR-PSNR22.91—Unverified
7PhySGHDR-PSNR21.81—Unverified